"""
Molecular Structure Renderer.

This module provides molecular structure visualization using RDKit:
- 2D structure rendering
- Morgan fingerprint visualization
- Molecular property calculation

For web-based 3D visualization, we also support 3Dmol.js integration.
"""

import io
import base64
from typing import Optional, Dict, Any, Tuple, List
from dataclasses import dataclass


@dataclass
class MoleculeInfo:
    """Container for molecular information."""
    smiles: str
    name: Optional[str] = None
    
    # Calculated properties
    molecular_weight: Optional[float] = None
    logp: Optional[float] = None
    hbd: Optional[int] = None  # H-bond donors
    hba: Optional[int] = None  # H-bond acceptors
    tpsa: Optional[float] = None  # Topological polar surface area
    rotatable_bonds: Optional[int] = None
    
    # Rendered images
    structure_2d_svg: Optional[str] = None
    structure_2d_png_base64: Optional[str] = None
    fingerprint_svg: Optional[str] = None


class MoleculeRenderer:
    """
    Renders molecular structures using RDKit.
    
    Provides:
    - 2D structure images (SVG and PNG)
    - Morgan fingerprint bit visualization
    - Basic property calculations
    """
    
    def __init__(self):
        """Initialize the renderer and check RDKit availability."""
        self._rdkit_available = self._check_rdkit()
    
    def _check_rdkit(self) -> bool:
        """Check if RDKit is available."""
        try:
            from rdkit import Chem
            from rdkit.Chem import Draw
            return True
        except ImportError:
            print("Warning: RDKit not installed. Molecular rendering disabled.")
            print("Install with: pip install rdkit")
            return False
    
    @property
    def is_available(self) -> bool:
        """Check if rendering is available."""
        return self._rdkit_available
    
    def parse_smiles(self, smiles: str) -> Optional[Any]:
        """
        Parse SMILES string to RDKit molecule object with enhanced error handling.
        
        Handles:
        - Standard SMILES parsing
        - SMILES with encoding issues (URL encoding, whitespace)
        - Complex stereochemistry
        - Salts and mixtures
        
        Args:
            smiles: SMILES notation
            
        Returns:
            RDKit Mol object or None if invalid
        """
        if not self._rdkit_available:
            return None
        
        if not smiles or not smiles.strip():
            return None
        
        from rdkit import Chem
        
        # Step 1: Clean and normalize SMILES
        clean_smiles = self._normalize_smiles(smiles)
        
        # Step 2: Try standard parsing
        mol = Chem.MolFromSmiles(clean_smiles)
        if mol is not None:
            return mol
        
        # Step 3: Try parsing without sanitization (for debugging)
        try:
            mol = Chem.MolFromSmiles(clean_smiles, sanitize=False)
            if mol is not None:
                # Try to sanitize manually
                try:
                    Chem.SanitizeMol(mol)
                    return mol
                except:
                    # Return unsanitized if that fails
                    pass
        except:
            pass
        
        # Step 4: Try stripping stereochemistry for complex molecules
        try:
            stripped_smiles = self._strip_stereochemistry(clean_smiles)
            mol = Chem.MolFromSmiles(stripped_smiles)
            if mol is not None:
                return mol
        except:
            pass
        
        # Step 5: For salt forms (e.g., "sodium salt"), try splitting
        if '.' in clean_smiles:
            # Take the largest fragment
            fragments = clean_smiles.split('.')
            largest = max(fragments, key=len)
            mol = Chem.MolFromSmiles(largest)
            if mol is not None:
                return mol
        
        return None
    
    def _normalize_smiles(self, smiles: str) -> str:
        """Normalize SMILES string by cleaning common issues."""
        import re
        import urllib.parse
        
        # Decode URL encoding if present
        if '%' in smiles:
            try:
                smiles = urllib.parse.unquote(smiles)
            except:
                pass
        
        # Remove whitespace and newlines
        smiles = smiles.strip().replace('\n', '').replace('\r', '').replace(' ', '')
        
        # Remove common prefixes/suffixes that might be added
        prefixes = ['SMILES:', 'smiles:', 'SMILES=', 'smiles=']
        for prefix in prefixes:
            if smiles.startswith(prefix):
                smiles = smiles[len(prefix):]
        
        # Fix common encoding issues
        smiles = smiles.replace('（', '(').replace('）', ')')
        smiles = smiles.replace('【', '[').replace('】', ']')
        smiles = smiles.replace('＝', '=').replace('＃', '#')
        
        return smiles
    
    def _strip_stereochemistry(self, smiles: str) -> str:
        """Strip stereochemistry from SMILES for fallback parsing."""
        import re
        # Remove @ symbols (stereochemistry markers)
        smiles = re.sub(r'@+', '', smiles)
        # Remove E/Z markers in double bonds
        smiles = re.sub(r'/|\\\\', '', smiles)
        return smiles
    
    def render_2d_svg(
        self,
        smiles: str,
        width: int = 400,
        height: int = 300,
        highlight_atoms: Optional[List[int]] = None,
    ) -> Optional[str]:
        """
        Render 2D structure as SVG.
        
        Args:
            smiles: SMILES notation
            width: Image width
            height: Image height
            highlight_atoms: Optional list of atom indices to highlight
            
        Returns:
            SVG string or None if failed
        """
        if not self._rdkit_available:
            return None
        
        from rdkit import Chem
        from rdkit.Chem import Draw
        from rdkit.Chem.Draw import rdMolDraw2D
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return None
        
        # Create drawer
        drawer = rdMolDraw2D.MolDraw2DSVG(width, height)
        
        # Set drawing options
        opts = drawer.drawOptions()
        opts.addStereoAnnotation = True
        opts.addAtomIndices = False
        
        # Draw molecule
        if highlight_atoms:
            drawer.DrawMolecule(mol, highlightAtoms=highlight_atoms)
        else:
            drawer.DrawMolecule(mol)
        
        drawer.FinishDrawing()
        svg = drawer.GetDrawingText()
        
        return svg
    
    def render_2d_png_base64(
        self,
        smiles: str,
        width: int = 400,
        height: int = 300,
    ) -> Optional[str]:
        """
        Render 2D structure as PNG and return base64 encoded string.
        
        Args:
            smiles: SMILES notation
            width: Image width
            height: Image height
            
        Returns:
            Base64 encoded PNG string or None if failed
        """
        if not self._rdkit_available:
            return None
        
        from rdkit import Chem
        from rdkit.Chem import Draw
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return None
        
        # Generate PNG image
        img = Draw.MolToImage(mol, size=(width, height))
        
        # Convert to base64
        buffer = io.BytesIO()
        img.save(buffer, format='PNG')
        buffer.seek(0)
        
        png_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
        
        return png_base64
    
    def get_data_uri(self, smiles: str, width: int = 400, height: int = 300) -> Optional[str]:
        """
        Get a data URI for embedding molecule image in HTML.
        
        Args:
            smiles: SMILES notation
            width: Image width
            height: Image height
            
        Returns:
            Data URI string or None
        """
        png_base64 = self.render_2d_png_base64(smiles, width, height)
        if png_base64:
            return f"data:image/png;base64,{png_base64}"
        return None
    
    def calculate_morgan_fingerprint(
        self,
        smiles: str,
        radius: int = 2,
        n_bits: int = 2048,
    ) -> Optional[List[int]]:
        """
        Calculate Morgan fingerprint (circular fingerprint).
        
        Args:
            smiles: SMILES notation
            radius: Fingerprint radius
            n_bits: Number of bits
            
        Returns:
            List of on-bit indices or None
        """
        if not self._rdkit_available:
            return None
        
        from rdkit import Chem
        from rdkit.Chem import AllChem
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return None
        
        fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=n_bits)
        
        # Get on-bits
        on_bits = list(fp.GetOnBits())
        
        return on_bits
    
    def render_fingerprint_bits(
        self,
        smiles: str,
        radius: int = 2,
        highlight_bits: Optional[List[int]] = None,
    ) -> Optional[str]:
        """
        Render Morgan fingerprint bit visualization as SVG.
        
        Shows which atoms contribute to specific fingerprint bits.
        
        Args:
            smiles: SMILES notation
            radius: Morgan fingerprint radius
            highlight_bits: Specific bits to highlight
            
        Returns:
            SVG string or None
        """
        if not self._rdkit_available:
            return None
        
        from rdkit import Chem
        from rdkit.Chem import AllChem, Draw
        from rdkit.Chem.Draw import rdMolDraw2D
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return None
        
        # Get bit info (which atoms contribute to which bits)
        bi = {}
        fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius, bitInfo=bi)
        
        # If specific bits requested, get atoms for those
        if highlight_bits:
            atoms_to_highlight = set()
            for bit in highlight_bits:
                if bit in bi:
                    for atom_info in bi[bit]:
                        center_atom, _ = atom_info
                        atoms_to_highlight.add(center_atom)
            highlight_atoms = list(atoms_to_highlight)
        else:
            highlight_atoms = None
        
        # Render with highlights
        return self.render_2d_svg(smiles, highlight_atoms=highlight_atoms)
    
    def calculate_properties(self, smiles: str) -> Optional[Dict[str, Any]]:
        """
        Calculate basic molecular properties.
        
        Args:
            smiles: SMILES notation
            
        Returns:
            Dictionary of properties or None
        """
        if not self._rdkit_available:
            return None
        
        from rdkit import Chem
        from rdkit.Chem import Descriptors, Lipinski
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return None
        
        return {
            "molecular_weight": round(Descriptors.MolWt(mol), 2),
            "logp": round(Descriptors.MolLogP(mol), 2),
            "hbd": Lipinski.NumHDonors(mol),
            "hba": Lipinski.NumHAcceptors(mol),
            "tpsa": round(Descriptors.TPSA(mol), 2),
            "rotatable_bonds": Lipinski.NumRotatableBonds(mol),
            "num_atoms": mol.GetNumAtoms(),
            "num_heavy_atoms": Lipinski.HeavyAtomCount(mol),
            "num_rings": Lipinski.RingCount(mol),
            "fraction_sp3": round(Lipinski.FractionCSP3(mol), 2),
        }
    
    def get_molecule_info(self, smiles: str, name: Optional[str] = None) -> MoleculeInfo:
        """
        Get comprehensive molecule information including rendered images.
        
        Args:
            smiles: SMILES notation
            name: Optional molecule name
            
        Returns:
            MoleculeInfo object with all available data
        """
        info = MoleculeInfo(smiles=smiles, name=name)
        
        if not self._rdkit_available:
            return info
        
        # Calculate properties
        props = self.calculate_properties(smiles)
        if props:
            info.molecular_weight = props["molecular_weight"]
            info.logp = props["logp"]
            info.hbd = props["hbd"]
            info.hba = props["hba"]
            info.tpsa = props["tpsa"]
            info.rotatable_bonds = props["rotatable_bonds"]
        
        # Render images
        info.structure_2d_svg = self.render_2d_svg(smiles)
        info.structure_2d_png_base64 = self.render_2d_png_base64(smiles)
        
        return info
    
    def identify_functional_groups(self, smiles: str) -> List[Dict[str, Any]]:
        """
        Identify reactive functional groups in a molecule using SMARTS patterns.
        
        This is crucial for compatibility analysis as it identifies
        potential reactive sites in the API molecule.
        
        Args:
            smiles: SMILES notation
            
        Returns:
            List of identified functional groups with properties
        """
        if not self._rdkit_available:
            return []
        
        from rdkit import Chem
        
        mol = self.parse_smiles(smiles)
        if mol is None:
            return []
        
        # Define SMARTS patterns for pharmaceutically relevant functional groups
        functional_group_patterns = {
            # Amines
            "primary_amine": {
                "smarts": "[NX3H2;!$([NX3H2]-C=O)]",
                "name_cn": "伯胺基团",
                "name_en": "Primary Amine",
                "property_type": "碱性",
                "reactions": ["美拉德反应（Maillard Reaction）", "氧化脱氨（Oxidative Deamination）", "席夫碱形成（Schiff Base）"],
            },
            "secondary_amine": {
                "smarts": "[NX3H1;!$([NX3H1]-C=O)]([#6])([#6])",
                "name_cn": "仲胺基团",
                "name_en": "Secondary Amine",
                "property_type": "碱性",
                "reactions": ["美拉德反应（Maillard Reaction）", "N-氧化（N-Oxidation）"],
            },
            "tertiary_amine": {
                "smarts": "[NX3H0;!$([NX3]-C=O)]([#6])([#6])([#6])",
                "name_cn": "叔胺基团",
                "name_en": "Tertiary Amine",
                "property_type": "碱性",
                "reactions": ["N-氧化（N-Oxidation）"],
            },
            # Thiols and Thioethers
            "thiol": {
                "smarts": "[SH]",
                "name_cn": "巯基",
                "name_en": "Thiol",
                "property_type": "中性/弱酸性",
                "reactions": ["氧化成二硫键（Disulfide Formation）", "金属配位（Metal Coordination）"],
            },
            "thioether": {
                "smarts": "[#6][SX2][#6]",
                "name_cn": "硫醚基团",
                "name_en": "Thioether",
                "property_type": "中性",
                "reactions": ["氧化成亚砜（Sulfoxide Formation）", "氧化成砜（Sulfone Formation）"],
            },
            # Hydroxyl groups
            "phenol": {
                "smarts": "[OX2H][c]",
                "name_cn": "酚羟基",
                "name_en": "Phenolic Hydroxyl",
                "property_type": "弱酸性",
                "reactions": ["氧化（Oxidation）", "光氧化（Photooxidation）", "醌形成（Quinone Formation）"],
            },
            "alcohol": {
                "smarts": "[OX2H][CX4]",
                "name_cn": "醇羟基",
                "name_en": "Aliphatic Hydroxyl",
                "property_type": "中性",
                "reactions": ["脱水（Dehydration）", "酯化（Esterification）"],
            },
            # Carbonyl groups
            "aldehyde": {
                "smarts": "[CX3H1](=O)[#6]",
                "name_cn": "醛基",
                "name_en": "Aldehyde",
                "property_type": "中性/亲电",
                "reactions": ["美拉德反应（Maillard Reaction）", "氧化成羧酸（Oxidation to Carboxylic Acid）"],
            },
            "ketone": {
                "smarts": "[CX3](=O)([#6])[#6]",
                "name_cn": "酮基",
                "name_en": "Ketone",
                "property_type": "中性",
                "reactions": ["还原（Reduction）", "缩合反应（Condensation）"],
            },
            # Carboxylic acid and derivatives
            "carboxylic_acid": {
                "smarts": "[CX3](=O)[OX2H]",
                "name_cn": "羧基",
                "name_en": "Carboxylic Acid",
                "property_type": "酸性",
                "reactions": ["盐形成（Salt Formation）", "酰胺化（Amidation）"],
            },
            "ester": {
                "smarts": "[CX3](=O)[OX2][#6]",
                "name_cn": "酯基",
                "name_en": "Ester",
                "property_type": "中性",
                "reactions": ["水解（Hydrolysis）", "转酯化（Transesterification）"],
            },
            "amide": {
                "smarts": "[CX3](=O)[NX3]",
                "name_cn": "酰胺基",
                "name_en": "Amide",
                "property_type": "中性",
                "reactions": ["水解（Hydrolysis）"],
            },
            "lactone": {
                "smarts": "[#6]1~[#6]~[#6](=O)~[OX2]~1",
                "name_cn": "内酯环",
                "name_en": "Lactone",
                "property_type": "中性",
                "reactions": ["开环水解（Ring-opening Hydrolysis）"],
            },
            # Nitrogen heterocycles
            "pyridine": {
                "smarts": "c1ccncc1",
                "name_cn": "吡啶环",
                "name_en": "Pyridine",
                "property_type": "碱性",
                "reactions": ["N-氧化（N-Oxidation）", "质子化（Protonation）"],
            },
            "imidazole": {
                "smarts": "c1cnc[nH]1",
                "name_cn": "咪唑环",
                "name_en": "Imidazole",
                "property_type": "碱性/两性",
                "reactions": ["N-氧化（N-Oxidation）", "金属配位（Metal Coordination）"],
            },
            # Other important groups
            "nitrile": {
                "smarts": "[CX2]#N",
                "name_cn": "氰基",
                "name_en": "Nitrile",
                "property_type": "中性",
                "reactions": ["水解成酰胺/羧酸（Hydrolysis）"],
            },
            "allylic": {
                "smarts": "[CX4][CX3]=[CX3]",
                "name_cn": "烯丙位",
                "name_en": "Allylic Position",
                "property_type": "中性",
                "reactions": ["自氧化（Autoxidation）"],
            },
            "benzylic": {
                "smarts": "[CX4H2]c",
                "name_cn": "苄位",
                "name_en": "Benzylic Position",
                "property_type": "中性",
                "reactions": ["自氧化（Autoxidation）"],
            },
        }
        
        identified_groups = []
        
        for group_id, group_info in functional_group_patterns.items():
            pattern = Chem.MolFromSmarts(group_info["smarts"])
            if pattern is None:
                continue
            
            matches = mol.GetSubstructMatches(pattern)
            if matches:
                identified_groups.append({
                    "id": group_id,
                    "name_cn": group_info["name_cn"],
                    "name_en": group_info["name_en"],
                    "property_type": group_info["property_type"],
                    "potential_reactions": group_info["reactions"],
                    "count": len(matches),
                    "atom_indices": [list(m) for m in matches],
                })
        
        return identified_groups
    
    def get_functional_groups_summary(self, smiles: str) -> str:
        """
        Get a formatted text summary of identified functional groups.
        
        Args:
            smiles: SMILES notation
            
        Returns:
            Formatted string for use in prompts
        """
        groups = self.identify_functional_groups(smiles)
        
        if not groups:
            return "未能识别到特征官能团，请人工确认分子结构"
        
        lines = []
        for g in groups:
            count_str = f"×{g['count']}" if g['count'] > 1 else ""
            lines.append(f"{g['name_cn']}（{g['name_en']}）{count_str} - {g['property_type']}")
        
        return "；".join(lines)


def get_3dmol_script(smiles: str, container_id: str = "mol3d") -> str:
    """
    Generate JavaScript for 3Dmol.js visualization.
    
    This returns a script that can be embedded in HTML to show
    an interactive 3D molecular viewer.
    
    Args:
        smiles: SMILES notation
        container_id: HTML container element ID
        
    Returns:
        JavaScript code string
    """
    # Note: This requires 3Dmol.js to be loaded in the page
    # and a valid SDF/MOL block. For simplicity, we use
    # the SMILES directly and let 3Dmol parse it.
    
    return f"""
<script>
(function() {{
    let viewer = $3Dmol.createViewer(document.getElementById('{container_id}'), {{
        backgroundColor: 'white'
    }});
    
    // Use PubChem to get 3D structure from SMILES
    // Alternatively, generate conformer with RDKit
    let smiles = '{smiles}';
    
    // Add molecule from SMILES (requires 3Dmol.js SmilesParser)
    viewer.addModel(smiles, 'smi');
    viewer.setStyle({{}}, {{stick: {{}}}});
    viewer.zoomTo();
    viewer.render();
}})();
</script>
"""


def get_3dmol_html(
    smiles: str,
    width: int = 400,
    height: int = 300,
) -> str:
    """
    Generate complete HTML for 3Dmol.js visualization.
    
    Args:
        smiles: SMILES notation
        width: Viewer width
        height: Viewer height
        
    Returns:
        Complete HTML string
    """
    return f"""
<div id="mol3d-container" style="width: {width}px; height: {height}px; position: relative;">
    <div id="mol3d" style="width: 100%; height: 100%;"></div>
</div>
<script src="https://3dmol.org/build/3Dmol-min.js"></script>
{get_3dmol_script(smiles, 'mol3d')}
"""


# Singleton instance for easy import
renderer = MoleculeRenderer()